AWS Big Data

MARCH 21, 2024

The company’s business analysts want to generate metrics to identify ticket movement over time, success rates for sellers, and the best-selling events, venues, and seasons. They would like to get these metrics in near real time using a zero-ETL integration. Choose Create policy.

Data Warehouse 100

Data Warehouse Metrics Statistics Optimization 100

AWS Big Data

MARCH 12, 2024

AWS Glue Data Quality reduces the effort required to validate data from days to hours, and provides computing recommendations, statistics, and insights about the resources required to run data validation. Create and attach a new inline policy ( AWSGlueDataQualityBucketPolicy ) with the following content.

Data Quality 103

Data Quality Measurement Testing Visualization 103

Domino Data Lab

APRIL 21, 2021

In contrast, the decision tree classifies observations based on attribute splits learned from the statistical properties of the training data. Machine Learning-based detection – using statistical learning is another approach that is gaining popularity, mostly because it is less laborious. from sklearn import metrics.

Statistics 69

Statistics Machine Learning Modeling Metrics 69

The Unofficial Google Data Science Blog

APRIL 23, 2024

the weight given to Likes in our video recommendation algorithm) while $Y$ is a vector of outcome measures such as different metrics of user experience (e.g., Experiments, Parameters and Models At Youtube, the relationships between system parameters and metrics often seem simple — straight-line models sometimes fit our data well.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

AWS Big Data

APRIL 3, 2024

Many organizations already use AWS Glue Data Quality to define and enforce data quality rules on their data, validate data against predefined rules , track data quality metrics, and monitor data quality over time using artificial intelligence (AI). The metrics are saved in Amazon S3 to have a persistent output. onData(df).useRepository(metricsRepository).addCheck(

Data Quality 103

Data Quality Visualization Metadata Metrics 103

Sisense

DECEMBER 11, 2019

He outlined how critical measurable results are to help VCs make major investment decisions — metrics such as revenue, net vs gross earnings, sales , costs and projections, and more. From a startup in 2012, it is now valued at $3.2 The company has integrated data analysis throughout its organization to power decision making.

Data Lake 80

Data Lake Big Data Sales Data-driven 80

Depict Data Studio

APRIL 10, 2023

and implications of findings) than in statistical significance. I first learned about this technique through Cole Nussbaumer’s Storytelling with Data workshop back in 2012—but geez, was it tough to apply! Dashboards provide key metrics about a program, department, or organization, usually at regular intervals over time (e.g.,

Visualization 105

Visualization Dashboards Testing Reporting 105

The Unofficial Google Data Science Blog

JUNE 30, 2016

Identification We now discuss formally the statistical problem of causal inference. We start by describing the problem using standard statistical notation. The field of statistical machine learning provides a solution to this problem, allowing exploration of larger spaces. For a random sample of units, indexed by $i = 1.

Statistics 45

Statistics Optimization Modeling Experimentation 45

DataRobot Blog

APRIL 4, 2022

MMM stands for Marketing Mix Model and it is one of the oldest and most well-established techniques to measure the sales impact of marketing activity statistically. As with any type of statistical model, data is key and GIGO (“Garbage In, Garbage Out”) principle definitely applies. What is MMM? Data Requirements.

Machine Learning 52

Machine Learning Sales Measurement ROI 52

The Unofficial Google Data Science Blog

MAY 31, 2016

This means it is possible to specify exactly in which geos an ad campaign will be served – and to observe the ad spend and the response metric at the geo level. In other words, iROAS is the slope of a curve of the response metric plotted against the underlying advertising spend. They are non-overlapping geo-targetable regions.

Advertising 43

Advertising Testing Sales Statistics 43

Domino Data Lab

MARCH 3, 2019

What metrics are used to evaluate success? While image data has been the stalwart for deep learning use cases since the proverbial “ AlexNet moment ” in 2011-2012, and a renaissance in NLP over the past 2-3 years has accelerated emphasis on text use cases, we note that structured data is at the top of the list in enterprise.

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

datapine

MAY 17, 2021

Without a doubt, the best way to drive maximum value from the metrics, insights, and information is through something called data discovery. Studies say that more data has been generated in the last two years than in the entire history before and that since 2012 the industry has created around 13 million jobs around the world.

Visualization 161

Visualization Data-driven Business Intelligence Dashboards 161

The Unofficial Google Data Science Blog

OCTOBER 12, 2017

1]" Statistics, as a discipline, was largely developed in a small data world. With more features come more potential post hoc hypotheses about what is driving metrics of interest, and more opportunity for exploratory analysis. Data was expensive to gather, and therefore decisions to collect data were generally well-considered.

Experimentation 62

Experimentation Testing Statistics Metrics 62

Occam's Razor

SEPTEMBER 21, 2015

Second, between 2012 and 2013. You are comparing 2012 and 2013, add a row of data at the top that shows your computation of the size of the opportunity for 2014. conversion rate (it might not be statistically significant!). Despite that, I bet it was still harder than necessary for you to figure out what is going on.

Visualization 161

Visualization Key Performance Indicator Slice and Dice Strategy 161

Domino Data Lab

JULY 22, 2019

He was saying this doesn’t belong just in statistics. It involved a lot of work with applied math, some depth in statistics and visualization, and also a lot of communication skills. I went to a meeting at Starbucks with the founder of Alation right before they launched in 2012, drawing on the proverbial back-of-the-napkin.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

Alation

FEBRUARY 20, 2020

Metrics and statistics are wonderful, but we need to surround data with more context and lower the costs of using data. Rather than focusing on making data consumers do more work, maybe we can boost literacy by surrounding the data with context and reducing the burden of understanding the information.

Big Data 52

Big Data Data-driven Statistics Metrics 52